Da Yan

Bio: Da Yan is an academic researcher from Tsinghua University. The author has contributed to research in topics: Energy consumption & Efficient energy use. The author has an hindex of 35, co-authored 125 publications receiving 4620 citations.

A review of uncertainty analysis in building energy assessment

Abstract: Uncertainty analysis in building energy assessment has become an active research field because a number of factors influencing energy use in buildings are inherently uncertain. This paper provides a systematic review on the latest research progress of uncertainty analysis in building energy assessment from four perspectives: uncertainty data sources, forward and inverse methods, application of uncertainty analysis, and available software. First, this paper describes the data sources of uncertainty in building performance analysis to provide a firm foundation for specifying variations of uncertainty factors affecting building energy. The next two sections focus on the forward and inverse methods. Forward uncertainty analysis propagates input uncertainty through building energy models to obtain variations of energy use, whereas inverse uncertainty analysis infers unknown input factors through building energy models based on energy data and prior information. For forward analysis, three types of approaches (Monte Carlo, non-sampling, and non-probabilistic) are discussed to provide sufficient choices of uncertainty methods depending on the purpose and specific application of a building project. For inverse analysis, recent research has concentrated more on Bayesian computation because Bayesian inverse methods can make full use of prior information on unknown variables. Fourth, several applications of uncertainty analysis in building energy assessment are discussed, including building stock analysis, HVAC system sizing, variations of sensitivity indicators, and optimization under uncertainty. Moreover, the software for uncertainty analysis is described to provide flexible computational environments for implementing uncertainty methods described in this review. This paper concludes with the trends and recommendations for further research to provide more convenient and robust uncertainty analysis of building energy. Uncertainty analysis has been ready to become the mainstream approach in building energy assessment although a number of issues still need to be addressed.

The Environmental Protection Agency

Abstract: The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

A review of data-driven building energy consumption prediction studies

Abstract: Energy is the lifeblood of modern societies. In the past decades, the world's energy consumption and associated CO 2 emissions increased rapidly due to the increases in population and comfort demands of people. Building energy consumption prediction is essential for energy planning, management, and conservation. Data-driven models provide a practical approach to energy consumption prediction. This paper offers a review of the studies that developed data-driven building energy consumption prediction models, with a particular focus on reviewing the scopes of prediction, the data properties and the data preprocessing methods used, the machine learning algorithms utilized for prediction, and the performance measures used for evaluation. Based on this review, existing research gaps are identified and future research directions in the area of data-driven building energy consumption prediction are highlighted.

State of the art in building modelling and energy performances prediction: A review

Abstract: In the European Union, the building sector is one of the largest energy consumer with about 40% of the final energy consumption. Reducing consumption is also a sociological, technological and scientific matter. New methods have to be devised in order to support building professionals in their effort to optimize designs and to enhance energy performances. Indeed, the research field related to building modelling and energy performances prediction is very productive, involving various scientific domains. Among them, one can distinguish physics-related fields, focusing on the resolution of equations simulating building thermal behaviour and mathematics-related ones, consisting in the implementation of prediction model thanks to machine learning techniques. This paper proposes a detailed review and discussion of these works. First, the approaches based on physical (‘‘white box’’) models are reviewed according three-category classification. Then, we present the main machine learning (‘‘black box’’) tools used for prediction of energy consumption, heating/cooling demand, indoor temperature. Eventually, a third approach called hybrid (‘‘grey box’’) method is introduced, which uses both physical and statistical techniques. The paper covers a wide range of research works, giving the base principles of each technique and numerous illustrative examples